This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This paper critically examined nine different software models for modeling / developing multi-agent based
systems. The study revealed that the different models examined have their various advantages /
disadvantages and uniqueness in terms of practical development/deployment of the multi-agent based
information system in question. The agentology model is one methodology that can be easily adopted or
adapted for the development of any multi-agent based software prototype because it was inspired by the
best practices and good ideas contained in other agent oriented methodologies like CoMoMas, MASE,
GAIA, Prometheus, HIM, MASim and Tropos.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneousl y using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multiproblem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comprehensive Testing Tool for Automatic Test Suite Generation, Prioritizatio...CSCJournals
Testing has been an essential part of software development life cycle. Automatic test case and test data generation has attracted many researchers in the recent past. Test suite generation is the concept given importance which considers multiple objectives in mind and ensures core coverage. The test cases thus generated can have dependencies such as open dependencies and closed dependencies. When there are dependencies, it is obvious that the order of execution of test cases can have impact on the percentage of flaws detected in the software under test. Therefore test case prioritization is another important research area that complements automatic test suite generation in objects oriented systems. Prior researches on test case prioritization focused on dependency structures. However, in this paper, we automate the extraction of dependency structures. We proposed a methodology that takes care of automatic test suite generation and test case prioritization for effective testing of object oriented software. We built a tool to demonstrate the proof of concept. The empirical study with 20 case studies revealed that the proposed tool and underlying methods can have significant impact on the software industry and associated clientele.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This paper critically examined nine different software models for modeling / developing multi-agent based
systems. The study revealed that the different models examined have their various advantages /
disadvantages and uniqueness in terms of practical development/deployment of the multi-agent based
information system in question. The agentology model is one methodology that can be easily adopted or
adapted for the development of any multi-agent based software prototype because it was inspired by the
best practices and good ideas contained in other agent oriented methodologies like CoMoMas, MASE,
GAIA, Prometheus, HIM, MASim and Tropos.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneousl y using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multiproblem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comprehensive Testing Tool for Automatic Test Suite Generation, Prioritizatio...CSCJournals
Testing has been an essential part of software development life cycle. Automatic test case and test data generation has attracted many researchers in the recent past. Test suite generation is the concept given importance which considers multiple objectives in mind and ensures core coverage. The test cases thus generated can have dependencies such as open dependencies and closed dependencies. When there are dependencies, it is obvious that the order of execution of test cases can have impact on the percentage of flaws detected in the software under test. Therefore test case prioritization is another important research area that complements automatic test suite generation in objects oriented systems. Prior researches on test case prioritization focused on dependency structures. However, in this paper, we automate the extraction of dependency structures. We proposed a methodology that takes care of automatic test suite generation and test case prioritization for effective testing of object oriented software. We built a tool to demonstrate the proof of concept. The empirical study with 20 case studies revealed that the proposed tool and underlying methods can have significant impact on the software industry and associated clientele.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
Presented at Artificial Intelligence and Machine Learning for Advanced Drug Discovery & Development 2019 on 28th May 2019 by Dr Ed Griffen of MedChemica Ltd
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...theijes
Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
Presented at Artificial Intelligence and Machine Learning for Advanced Drug Discovery & Development 2019 on 28th May 2019 by Dr Ed Griffen of MedChemica Ltd
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...theijes
Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
Phishing is the fraudulent acquisition of personal information like username, password, credit card information, etc. by tricking an individual into believing that the attacker is a trustworthy entity. It is affecting all the major sector of industry day by day with lots of misuse of user’s credentials. So in today
online environment we need to protect the data from phishing and safeguard our information, which can be done through anti-phishing tools. Currently there are many freely available anti-phishing browser extensions tools that warns user when they are browsing a suspected phishing site. In this paper we did a literature survey of some of the commonly and popularly used anti-phishing browser extensions by reviewing the existing anti-phishing techniques along with their merits and demerits.
The internet has been playing an increasingly important role in our daily life, with the availability of many web services such as email and search engines. However, these are often threatened by attacks from computer programs such as bots. To address this problem, CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) was developed to distinguish between computer programs and human users. Although this mechanism offers good security and limits automatic registration to web services, some CAPTCHAs have several weaknesses which allow hackers to infiltrate the mechanism of the CAPTCHA. This paper examines recent research on various CAPTCHA methods and their categories. Moreover it discusses the weakness and strength of these types.
OS VERIFICATION- A SURVEY AS A SOURCE OF FUTURE CHALLENGESIJCSES Journal
Formal verification of an operating system kernel manifests absence of errors in the kernel and establishes trust in it. This paper evaluates various projects on operating system kernel verification and presents indepth survey of them. The methodologies and contributions of operating system verification projects have been discussed in the present work. At the end, few unattended and interesting future challenges in
operating system verification area have been discussed and possible directions towards the challenge solution have been described in brief.
Design and Development of Arm-Based Control System for Nursing Bed IJCSES Journal
This paper introduces a kind of ARM embedded system as the control systemof the nursing bed.The
embedded control system takes the ARM9 S3C2440 chip as the core of data processing. The design ofthe
control system includes hardware design, software design and PC monitoring system design.
STATE-OF-THE-ART IN EMPIRICAL VALIDATION OF SOFTWARE METRICS FOR FAULT PRONEN...IJCSES Journal
With the sharp rise in software dependability and failure cost, high quality has been in great demand.However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on their development has become increasingly difficult, time and resource consuming activity. Consequently, it becomes inevitable to deliver software that have no serious faults. In
this case, object-oriented (OO) products being the de facto standard of software development with their unique features could have some faults that are hard to find or pinpoint the impacts of changes. The earlier faults are identified, found and fixed, the lesser the costs and the higher the quality. To assess product quality, software metrics are used. Many OO metrics have been proposed and developed. Furthermore,
many empirical studies have validated metrics and class fault proneness (FP) relationship. The challenge is which metrics are related to class FP and what activities are performed. Therefore, this study bring together the state-of-the-art in fault prediction of FP that utilizes CK and size metrics. We conducted a systematic literature review over relevant published empirical validation articles. The results obtained are
analysed and presented. It indicates that 29 relevant empirical studies exist and measures such as complexity, coupling and size were found to be strongly related to FP.
Loan financings belong and parcel of any type of human being when the entire globe is viewing an uptrend of costs for all important commodities permanently to go perfectly. The increase in cost has actually been so high that every person is feeling the crunch of money to stabilize the need and the supply.
In order to achieve the wide range of the robotic application it is necessary to provide iterative motions
among points of the goals. For instance, in the industry mobile robots can replace any components between
a storehouse and an assembly department. Ammunition replacement is widely used in military services.
Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if
it is necessary to observe control points in the secret place. The paper deals with path planning programme
for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the
design of modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data obtained
using the motion-planning. The results of the careful analysis were considered for optimal path planning
algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for
steady covered space. The results described in this work can be extended in a number of directions, and
applied to other algorithms.
Cloud computing is a technique that has a great capabilities and benefits for users. Cloud characteristics
encourage many organizations to move to this technology. But many consideration faces transmission
process. This paper outline some of these considerations and considerable efforts solved cloud scalability
issues.
בעיצוב חווית משתמש צריך תמיד לקחת בחשבון את ההשפעה שלנו על המשתמש, כיצד אנחנו עושים לו נעים, מה מעניין אותו, איך נראית המציאות שלו, מה יהיה לו אינטואיטיבי..
עשינו מצגת לסקירה כיצד הטכנולוגיה מעצבת את המציאות שלנו, ממנה אפשר ללמוד כמה דברים על החוויה שלנו עם מגוון ממשקים בחיי היום יום
This paper explains new imaging techniques that show promising results in breast cancer detection. The
presented techniques use microwave-based methods, wavelet analyses, and neural networks to get a
suitable resolution for the breast image. One of the presented techniques (hybrid method) uses a
combination of microwaves and acoustic signals to improve the detection capability. Some promising
results are shown and explained.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Software testing is an important activity of the software development process. Software testing is most
efforts consuming phase in software development. One would like to minimize the effort and maximize the
number of faults detected and automated test case generation contributes to reduce cost and time effort.
Hence test case generation may be treated as an optimization problem In this paper we have used genetic
algorithm to optimize the test case that are generated applying conditional coverage on source code. Test
case data is generated automatically using genetic algorithm are optimized and outperforms the test cases
generated by random testing.
Analysis of selection schemes for solving job shop scheduling problem using g...eSAT Journals
Abstract Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm. The choice of selection method to be applied for solving problems has a wide role in the Genetic algorithm process. The speed of convergence towards the optimum solution for the chosen problem is largely determined by the selection mechanism used in the Genetic algorithm. Depending upon the selection scheme applied, the population fitness over the successive generations could be improved. There are various type of selection schemes in genetic algorithm are available, where each selection scheme has its own feasibility for solving a particular problem. In this study, the selection schemes namely Stochastic Universal Sampling (SUS), Roulette Wheel Selection (RWS), Rank Based Roulette Wheel Selection (RRWS) and Binary Tournament Selection (BTS) were chosen for implementation. The characteristics of chosen selection mechanisms of Genetic algorithm for solving the job shop scheduling problem were analyzed. The Genetic algorithm with four different selection schemes is tested on instances of 7 benchmark problems of different size. The result shows that the each of the four selection schemes of Genetic algorithm have been successfully applied to the job shop scheduling problems efficiently and the performance of Stochastic Universal Sampling selection method is better than all other four selection schemes. Keywords: Genetic Algorithm, Makespan, Selection schemes
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...cscpconf
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL IJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
1. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016
DOI:10.5121/ijcses.2016.7203 21
SOFTWARE TESTING USING GENETIC ALGORITHMS
Akshat Sharma1
, Rishon Patani1
and Ashish Aggarwal1
1
School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu,
India
ABSTRACT
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
KEYWORDS
Genetic algorithm, Fitness function, Test data.
1. INTRODUCTION
Software testing is a process in which the runtime quality and quantity of a software is tested to
maximum limits. The basic test of software is done in the environment for which it is has been
designed. It’s run through is checked for correct and efficient outputs. Also software testing is
done in foreign environments also so as to explore about the possibilities of scalability [8, 12].
Each software is tested under various strategized environments.
The testing done is expected to produce the correct results under the assumptions of specific
functions, but at no time all the defects can be identified. Instead, it furnishes a comparison that
compares the state and behavior of the product —principles or mechanisms by which users might
be able to recognize the problem.
A test case is generally the data which acts as input for the software testing to done. Its consists of
unique identifier, requirement references from a software specification, a series of steps to follow,
events, preconditions, input, output, expected result, and actual result.[17] It is also coined as an
expected output to the tested environment. This can be as pragmatic as “for condition your
derived result is b”, whereas other test cases described the input scenario in detailed analysis and
showed results that were expected [2, 9, 16].
Evolutionary Testing uses a kind of meta-heuristic search technique, the Genetic Algorithm (GA),
to convert the task of test case generation into an optimal problem [4, 13, 27]. Evolutionary
2. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016
22
testing is used to search for optimal test parameter combinations that satisfy a predefined test
criterion. This test criterion is represented by using a “cost function” that measures how well each
of the automatically generated optimization parameters are satisfying the given test criterion [7,
22].
In our paper a study of different types of genetic algorithms is done. Different algorithms have
been run on different tools and analyzed for their performance. All these algorithms follow the
same basis of evolutionary testing but have different cost functions. On running these cost
functions on different tools, observations on how these functions respond are made [1, 3, 5, 11].
2. INTRODUCTION TO GENETIC ALGORITHM
Genetic algorithms are one of the best ways to solve a set of problems for which little information
is given. Genetic algorithms are a very general algorithm and so they will work well in any search
space [1, 25, 30, 33]. All you need to know is what you need the solution to be able to do well,
and a genetic algorithm will be able to create a high quality solution. Genetic algorithms use the
principles of selection and evolution to produce solution for various complex problems.
Genetic algorithms tends to thrive in an environment in which there is a very large set of
candidate solutions and in which the search space is not favorable and has many hills and
valleys[12,15,16]. True, genetic algorithms can do well in any environment, but they might be
greatly outclassed by more situation specific algorithms in the simpler search spaces. Therefore
you must keep in mind that genetic algorithms are not always the best choice in random
scenarios. Sometimes they might take quite a while to run and are therefore not always feasible
for real time use. They are, however, one of the most powerful methods with which high quality
solutions are created quickly to a problem [4,8,21]. There are few basic
methodology/Terminology that will be used while implementing genetic algorithm like:
Individual – Possible solutions
Population - Set of all individuals
Search Space - All possible solutions to the specified problem
Chromosome – Blueprint for an individual
Trait - Possible aspect of an individual entity.
Allele - Possible settings for a trait
Locus - The position of a gene on the chromosome
Genome - Collection of all chromosomes for an individual entity.
3. BACKGROUND
Genetic algorithms use the following three operations on its population.
3.1 Selection:
A selection process is applied to determine a way in which individuals are chosen for mating from
a population based on their fitness. Fitness is defined as a characteristic and capability of an
individual to survive and reproduce in an environment. Selection generates the new population
from the old one, thus starting a new generation. The fitness value of each chromosome in present
3. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016
23
generation is determined by an appropriate evaluation. Thus, the fitness value is used to select a
set of better chromosomes from the population for the next generation.[5,6].
3.2 Crossover:
After the selection process, the crossover operation is applied to the chromosomes selected from
the population. Crossover involves swapping of sequence of bits or genes in the string between
two individuals [8, 10]. This process of swapping is carried out and repeated each time with
different parent individuals until the next generation has optimum individuals.
Figure 3.2: Uniform Crossover
3.3 Mutation:
After the crossover process, the mutate operation is applied to a randomly select subset of the
population. Mutation leads to an alteration of chromosomes in small new ways to introduce good
traits. The main aim of mutation is to bring diversity in population.[1,5]
Figure 3.3: Mutation (Bit Inversion)
Factors essential in a fitness function are:
• Likelihood.
• Close to Boundary Value.
• Branch Coverage.
It has been proven that GAs required less CPU time in reaching a global solution in software
testing [13].
3.4 Need for Genetic Algorithms in Software Testing:
Drawbacks of manual testing: [7, 12]
Speed of operation is limited as it is carried out by humans.
High investment in terms of cost, time.
Limited availability of resources.
4. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016
24
Redundancy in test cases.
Inefficient and inaccurate test checking.
Pros of using genetic algorithms in software testing:
Parallelism is a important characteristic of genetic testing [11,19].
Less likely to get stuck in extreme ends of a code during testing since it operates in a search
space.
With the same encoding, only fitness function needs to be changed according to the problem.
Figure 4.1: Test Case Generation in Software Testing Using GA
4. GENETIC ALGORITHM WORKING
The genetic algorithm is an evolutionary approach to computing, which has the ability to
determine appropriate approx. solutions to optimization problems. The basic process adopted by
Genetic algorithms typically involves creating an initial set of random solutions (population) and
evaluating them [2, 5, 9, 12]. Followed by a process of selection, the better solutions are
identified (parents) and are then used to generate new solutions (children). These values can be
used to replace other lesser members of the population. This new population (generation), is then
reevaluated and the process for generating new values continues, reproducing new generations
until either a final solution is determined or some other criterion for determination of result is
reached[18,22,32].
Genetic Algorithm borrows its terms from the biological world. For example, Genetic algorithm
uses different representations for potential solutions which are referred to as a chromosome and
the operators that are used to generate new child solutions are such as crossover and mutation are
derived from nature.
In their generic and most basic form, Genetic Algorithms were used mainly for single objective
search and optimization algorithms. Common to most Genetic algorithms is the use of a
chromosome, genetic operators, a selection mechanism and an evaluation mechanism [23, 27].
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In our case, all we have to do is determine the parameters we need in order to build an efficient
function and bundle them into a binary string, this will be the definition of our chromosome.
Therefore, each chromosome will fully describe a Function.
A common approach when working with Genetic Algorithm is to start by making a 'population' of
random chromosomes (Test Variables) perhaps a 100. You may remember earlier that we said we
can test each one individuals and score it, or to use the correct terminology, 'evaluate its fitness’
(function).
This calculated Fitness function will help us to evaluate the efficiency of the method used [11, 27,
29].And further to increase the efficiency of the test result we can change the input parameters
and obtain values for different number of population.
5. APPLICATIONS OF GENETIC ALGORITHM
Genetic Algorithm have been used for solving complex problems (such as NPC and NP-hard), for
machine learning and is also used for evolving simple test programs. They are a very effective
way of quickly finding a reasonable solution to a complex problem.
Genetic algorithms are most efficient and effective in a search space for which little is known.
Then again, genetic algorithms can be used to produce solutions to problems working only in the
test environment and deviates once you try to use them in the real world [17, 24].
So when put simply, genetic algorithm can be used to create solutions for problems that are not
very easy to calculate and analyze.
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Figure 5.0: Flow chart of the workflow of Genetic Algorithm used for Test Case Generation n Software
Testing.
6. IMPLEMENTATION OF GAIN SOFTWARE TESTING
6.1 Test case generation using GA in Ruby
Algorithm:
Start with randomly generated test cases from the population.
Calculate the fitness f(x) of each pair of test cases (chromosome x) in the population.
Repeat the following steps until a n child test cases have been generated.
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a. Select a pair of parent test cases from the current population where the probability of selection
is an increasing function of fitness. Selection is done “with replacement,” meaning that the same
pair of test case can be selected more than once to become a parent. i.e. (Selection process is
carried out)
b. With the crossover probability Pc, cross over the pair at a randomly chosen point to form two
child cases or off springs. If no crossover takes place, form two test cases that are exact copies of
their respective parent cases.
c. Mutate the two child cases with mutation probability Pm, and place the resulting pair of test
cases in the new population. If n is odd, one new population member can be discarded at random.
Replace the current test cases with the new test cases.
6.1.1
Population size = 50
Number of generations = 500
Crossover rate=0.7
Mutation rate = 0.001
Ruby Implementation with First Case:
Generation Average fitness Max fitness
1 31.88 33
2 32.88 34
3 32.67 34
4 32.75 35
5 32.96 34
10 34.17 38
25 37.29 42
50 41.21 44
100 39.67 47
150 44.33 49
200 46.33 47
250 47.63 49
500 50.23 50
750 51.79 51
1000 52 51
Table 1: Average fitness value with mutation rate = 0.001
6.1.2
Population size = 50
Number of generations = 500
Crossover rate=0.7
Mutation rate = 0.01
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Ruby Implementation with Second Case:
Generation Average Max
1 32.25 32
2 32.13 32
3 33.92 33
4 32.42 33
5 33.79 33
10 34.71 34
25 38.42 36
50 39.08 37
100 37.42 36
150 35.54 40
200 38.79 40
250 41.83 39
500 42.18 43
Table 2: Average fitness value with mutation rate = 0.01
Mutation rate has a great impact on the average on the average fitness of genetic algorithms
during testing. Smaller the rate, better the fitness function value will be.
Below is a graph which represents the average fitness overtime for different mutation rates.
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6.2 Genetic Algorithm Implementation in C++
Pseudo-code for genetic algorithm:
choose initial_population:
evaluate individual_fitness function
determine population’s_average
fitness_function
Repeat
select best_case individuals to
reproduce;
mate_pairs at random;
apply crossover_operator;
apply mutation_operator;
evaluate Individual fitness;
determine population's average
fitness;
The second step consists for generating data consists of the outer loop, which will generate the
possible test cases remaining. To account for the possibility of unfeasible test requirements, which
includes branches and statement values, the loop will produces iterations until it satisfies the test
results for the given population values. The algorithm will produce values which will be applied
for the crossover and mutation operator. Then the fitness function for individual values are
generated and the population’s individual average fitness functions in calculated.
In the final step, the algorithm will assign the combined values of the test cases and find at least
one individual desired fitness function values until enough test generations have been passed.
6.2.1
Population size = 50
Number of generations = 250
Crossover rate=0.7
Mutation rate = 0.001
Table 3: Best fitness value generation
Iteration Best fitness Average fitness Standard Deviation
1 16.77 11.59 5.54
2 20.39 15.09 3.26
3 20.39 15.71 3.27
4 22.99 15.84 3.79
5 22.99 16.03 3.89
10 23.24 17.65 3.46
25 25.12 20.87 3.61
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50 25.89 21.42 4.32
100 26.27 20.88 5.33
150 26.77 23.28 3.77
200 26.77 20.38 6.91
250 26.78 22.41 4.42
500 26.98 22.68 5.54
Graph: Number of generations (x-axis) vs. average fitness (y-axis)
6.3 Genetic Algorithm Implementation using Matlab
In this work both the genetic algorithm and the random testing method were compared and
detailed analysis of the best fitness has been evaluated. In order to compare Genetic algorithm
and a pure random method, 150 test cases were generated and tested by both methods [22]. From
this we can see that the average response time of test cases created by genetic algorithm is much
efficient than that of the random method.
Case 1 shows the plot of the best and mean score of the population at every generation. The
second plot function is stopping criteria, which plots the percentage of stopping criteria satisfied.
Figure 1
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Results generated for Case 1:
The number of generations: 124
The number of function evaluations: 6250
The best function value found: -186.
Case 2 shows a better detailed analysis of the best and the mean fitness function by changing the
test cases to 20 instead of 10. This iteration generates better results than the previous iteration and
by further iterating the test cases the result is obtained.
Figure 2
Results generated for Case 2:
The number of generations: 68
The number of function evaluations: 650
The best function value found: -176.997
Case 3
Figure 3
Results generated for final Case 3:
The number of generations was: 101
The number of function evaluations was: 5100
The best function value found was: -186.627
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